---

name: polymarket-smart-money description: "Discover, analyze, and filter Polymarket smart money wallets. Use when asked to find profitable traders, analyze wallet addresses, detect market makers or HFT bots, assess copy-trading reliability, run the smart money discovery pipeline, or check if a Polymarket address is suitable for copy trading. Triggers on: find smart money, analyze wallet, discover traders, is this a market maker, copy trading, leaderboard, 聪明钱, 做市商检测, 跟单可靠性."

Polymarket Smart Money Discovery & Analysis

Discover profitable Polymarket traders, filter out market makers and HFT bots, and assess copy-trading reliability.

Project Location

{baseDir}/../../agents/polymarket-bot/PolyAnalysis/

Activate the virtualenv before running any script:

cd {baseDir}/../../agents/polymarket-bot/PolyAnalysis
source .venv/bin/activate

Quick Commands

Analyze a single wallet

python analyze.py <address>
python analyze.py <address> --force   # force full refresh
python analyze.py <address> --json    # JSON output

The report includes:

• PnL, ROI, win rate
• Entry timing analysis
• Copy trading score (0-100)
• MM/HFT detection (MM Score, is_market_maker, is_hft)
• Copy reliability label (🟢 高可靠 / 🟡 中可靠 / 🔴 低可靠 / ❌ 不可跟单)

Discover smart money (batch)

python analyze.py discover --top-n 10
python analyze.py discover --top-n 20 --min-profit 100000 --min-volume 500000
python analyze.py discover --top-n 10 --output-json --output-file smart_money.json

Discovery pipeline:

1. Fetch from Polymarket v1 Leaderboard API (7 strategies)
2. Deduplicate across dimensions
3. Stage 1 pre-filter: PnL/Vol < 3% → exclude (likely market maker)
4. Fetch activity data for remaining addresses
5. Stage 2 deep analysis: MM Score + HFT detection
6. Exclude "❌ 不可跟单" addresses
7. Rank by copy trading score

Cache management

python analyze.py cache list
python analyze.py cache stats
python analyze.py cache clear <address>
python analyze.py cache clear --all

Filtering Pipeline

Stage 1: Pre-filter (from leaderboard data, zero cost)

Rule	Threshold	Rationale
PnL/Vol ratio	< 3% → exclude	Market makers earn tiny spreads on huge volume
VOL-only listing	exclude	Only on volume leaderboard, not on any PNL leaderboard

Stage 2: Deep analysis (from activity data)

MM Score (0-100, weighted)

Indicator	Weight	Market Maker Signal
PnL/Vol ratio	30%	< 1%
Decision frequency (positions/day)	25%	> 50/day
Buy/sell balance	20%	> 0.7 (near 1:1)
Avg holding time	15%	< 1 hour
Trade amount uniformity (CV)	10%	< 0.3

MM Score > 50 → flagged as market maker.

Important: Decision frequency uses positions per day, not trades per day. A directional trader may place hundreds of trades to build one position (dollar-cost averaging). What matters is how many independent position decisions they make.

HFT Detection (2 of 3 conditions)

Condition	Threshold
Decision frequency	> 10 positions/day
Median holding time	< 4 hours
Median position size	< $50

Copy Reliability Labels

Label	Conditions
🟢 高可靠	Holding > 24h, < 2 positions/day, PnL/Vol > 10%
🟡 中可靠	Holding 4-24h, < 5 positions/day
🔴 低可靠	Holding < 4h or > 5 positions/day
❌ 不可跟单	MM Score > 50 or HFT detected

Data Sources

API	Base URL	Auth	Use
Gamma API	gamma-api.polymarket.com	None	Markets, events, search
Data API v1	data-api.polymarket.com/v1	None	Leaderboard, positions, trades, PnL
CLOB API	clob.polymarket.com	For trading	Orderbook, prices

Key endpoints

GET /v1/leaderboard?category=OVERALL&timePeriod=ALL&orderBy=PNL&limit=50&offset=0
GET /positions?user={address}
GET /closed-positions?user={address}
GET /activity?user={address}
GET /profit-loss?user={address}

On-Chain Data (Phase 2)

Alchemy RPC configured in .env:

POLYGON_RPC_URL=https://polygon-mainnet.g.alchemy.com/v2/<key>

CTF Exchange contract: 0x4bFb41d5B3570DeFd03C39a9A4D8dE6Bd8B8982E

Key event: OrderFilled — extract maker/taker addresses and trade amounts from on-chain logs.

Free tier limit: 10 blocks per eth_getLogs call.

Architecture

polycopilot/
├── fetcher.py      # Data fetching (incremental + full)
├── processor.py    # Trade processing, MarketReport generation
├── analyzer.py     # Scoring, MM/HFT detection, copy reliability
├── discovery.py    # Multi-source wallet discovery + filtering
└── cache.py        # Disk cache for incremental updates

Interpreting Results

When presenting results to users:

• Always show the copy reliability label prominently
• For ❌ addresses, explain why (MM Score, HFT indicators)
• For 🟢 addresses, highlight key strengths (holding time, PnL/Vol efficiency)
• Compare addresses using the copy trading score (0-100, A/B/C/D grades)
• Use Chinese for user-facing output (the user prefers Chinese)